Matt Spangler, Bruce Golden, Larry Kuehn, Mark Thallman, Warren - - PowerPoint PPT Presentation

Matt Spangler, Bruce Golden, Larry Kuehn, Mark Thallman, Warren Snelling, Bob Weaber

▪ The global Animal Breeding and Genetics community has done a tremendous job at increasing scientific knowledge, developing selection tools, and delivering these tools to the US Beef Industry. ▪ Despite these advancements, technology adoption is embarrassingly poor.

▪ < 30% of producers use EPD (Weaber et al., 2014)

▪ Poor technology adoption is related to the sum of many underlying issues:

▪ Genetic prediction seems opaque ▪ Consultancy is often from sources other than what might be preferred ▪ Commercial producers do not have the needed time to excel in all areas, and focus on

day-to-day animal and financial management

▪ Combining all partial solutions is a very cumbersome task

▪ Breeding objective ▪ Breeding system ▪ Breed choice ▪ Trait emphasis ▪ Sire selection ▪ And all need to contemplate that which is economical and possible given environmental constraints

▪ USDA Funded CARE Grant ▪ Aim is to develop a web-based tool to aid in genetic selection decisions ▪ Initiated with an industry-wide survey in 2018 ▪ Advisory board of producers (commercial and seedstock), extension faculty, breed association staff

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▪ Online Survey of Beef Producers ▪ Fall/winter 2018-2019 ▪ 1,530 respondents

▪ Self selected ▪ Nationally publicized (Breed Assn., NCBA, Extension lists, etc.)

▪ 1,161 completed survey

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10 20 30 40 50 60 70 80 90 100 Owner Employee Manager Percent of Respondents

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5 10 15 20 25 30 35 40 45 Percent of Respondents

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5 10 15 20 25 30 <25 25-50 51-100 101-250 251-500 501-1000 >1000 Percentage of Respondents

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10 20 30 40 50 60 1-2 3-5 6-10 11-20 20 or more N/A Percent of Respondents

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5 10 15 20 25 30 35 40 45 No Response Strongly disagree Somewhat disagree Neither agree nor disagree Somewhat agree Strongly agree Percent of Respondents

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5 10 15 20 25 30 35 40 45 50 No Response Strongly disagree Somewhat disagree Neither agree nor disagree Somewhat agree Strongly agree Percent of Respondents

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10 20 30 40 50 60 No Response Strongly disagree Somewhat disagree Neither agree nor disagree Somewhat agree Strongly agree Percent of Respondents

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10 20 30 40 50 60 No Response Strongly disagree Somewhat disagree Neither agree nor disagree Somewhat agree Strongly agree Percent of Respondents

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10 20 30 40 50 60 70 80 No Response Not at all important Slightly important Moderately important Very important Extremely important Percent of Responsdents

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5 10 15 20 25 30 35 40 45 No Response Not at all important Slightly important Moderately important Very important Extremely important Percent of Respondents

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5 10 15 20 25 30 35 40 No Response None Not detailed Somewhat detailed Very detailed Percent of Respondents

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5 10 15 20 25 30 35 40 45 50 No Response None Not detailed Somewhat detailed Very detailed Percent of Respondents

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5 10 15 20 25 30 35 40 45 50 No Response Daily Less than

• nce per

week Never Once per week Rarely Twice or more daily Twice or more per week Percent of Respondents

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10 20 30 40 50 60 70 80 90 No Response FALSE TRUE Percent of Respondents

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5 10 15 20 25 30 35 40 45 No Response Definitely not Probably not Might or might not Probably yes Definitely yes Percent of Respondents

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5 10 15 20 25 30 35 40 45 No Response Strongly disagree Somewhat disagree Neither agree nor disagree Somewhat agree Strongly agree Percent of Respondents

▪ Tool to enable informed multiple-trait selection ▪ Based on:

▪ Breeding objectives ▪ Economic parameters ▪ Relationships among traits ▪ Population (herd) means

▪ Designed to improve commercial level profitability

▪ Develop a Breeding Objective

▪ Identifies sources of cost and revenue ▪ Sets goals conditioned on resources

▪ Identify breed(s) ▪ Develop a Breeding System ▪ Select seedstock supplier(s) ▪ Select bulls

▪ Should align with breeding objective

Data

Data is constantly growing (more animals, more traits, more genotypes, sequence data)

Knowledge

Requires turning data into tools This is where the global ABG community spends a great deal of time

▪ A lot of bull sales, and a lot of bulls in each sale ▪ Too many EPD—hard, if not impossible, to select on multiple traits simultaneously using only individual EPD ▪ In many cases EPD are breed-specific—must convert to common base ▪ Need to account for the value of heterosis and differences in breeds relative to average performance ▪ Indexes exist and are provided by breed associations (and some vendors)

▪ Although robust they are generalizations

Tools

Increasing list of EPD

Decisions

Requires turning tools into impactful decisions

▪ Producers face the problem of obtaining the best bulls for their operation in that given setting. ▪ ‘Best’ is a relative concept. ▪ A ‘less desirable’ bull may become the preferred choice over a ‘more desirable’ bull if his sale price discount is larger than the differential in value between the two bulls.

▪ We have framed three possible use cases:

▪ Commercial buyers (genetic purchasing decisions based on firm-specific

breeding objectives)

▪ Seedstock sellers (matching sale offering to individual customers) ▪ Seedstock buyers (matching genetic purchasing decisions to specified goals)

▪ (co)Variances—literature

▪ Cost/revenue pricing—industry averages or use- defined ▪ Breed information—user defined ▪ Phenotypic means—industry averages or user defined ▪ Breeding objectives—user defined ▪ EPD—Uploaded (user or seedstock seller), secure API breed association

Use case Breeding

• bjective

Herd-level parameters Identification of breeds/breeders Individual selection

▪ Tiered layer of input

▪ Essentially generalized index ▪ Reasonable knowledge of unit cost of production

▪ Discounted gene flow ▪ Discounted expression rates ▪ Planning horizon ▪ Can be used to create generalized indexes with ability to further “tweak” by members/users

▪ Alpha version with grant team ▪ Next steps

▪ Version to advisory board ▪ Key training sessions (extension personnel, breed association staff)

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▪ The impetus for this project is not the belief that currently available selection indices are so inherently flawed that they are of little value. ▪ We believe that allowing beef cattle producers to take part in the creation of their own selection index has the potential to increase the rate of technology adoption. ▪ The other primary improvement is in the ability to combine multiple partial solutions (e.g., additive and non-additive genetic effects) to enable sire selection across breeds in an economic framework.

USDA-AFRI-CARE Beef Cattle Production System Decision Support Tools to Enable Improved Genetic, Environmental, and Economic Resource Management Survey of Industry Stakeholders; Award Number: 2018-68008-27888

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